Overview

Dataset statistics

Number of variables14
Number of observations12575
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.4 MiB
Average record size in memory120.0 B

Variable types

Text2
Numeric12

Alerts

percentage0to15years is highly overall correlated with percentagehouseholdswithchildrenHigh correlation
percentage25to45years is highly overall correlated with percentage45to65years and 4 other fieldsHigh correlation
percentage45to65years is highly overall correlated with percentage25to45years and 1 other fieldsHigh correlation
percentage65yearsorolder is highly overall correlated with percentage25to45yearsHigh correlation
percentagehouseholdswithchildren is highly overall correlated with percentage0to15years and 1 other fieldsHigh correlation
percentagehouseholdswithoutchildren is highly overall correlated with percentage25to45years and 2 other fieldsHigh correlation
percentagenonwesternmigrationbackground is highly overall correlated with percentage25to45years and 4 other fieldsHigh correlation
percentageonepersonhouseholds is highly overall correlated with percentagehouseholdswithchildren and 3 other fieldsHigh correlation
percentagewesternmigrationbackground is highly overall correlated with percentagenonwesternmigrationbackground and 1 other fieldsHigh correlation
populationdensityperkm2 is highly overall correlated with percentage25to45years and 2 other fieldsHigh correlation
neighborhoodcode has unique valuesUnique
percentagenonwesternmigrationbackground has 1254 (10.0%) zerosZeros

Reproduction

Analysis started2024-07-05 09:50:45.659403
Analysis finished2024-07-05 09:51:09.753757
Duration24.09 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

neighborhoodcode
Text

UNIQUE 

Distinct12575
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size196.5 KiB
2024-07-05T11:51:09.953544image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters125750
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12575 ?
Unique (%)100.0%

Sample

1st rowBU00140000
2nd rowBU00140001
3rd rowBU00140002
4th rowBU00140003
5th rowBU00140005
ValueCountFrequency (%)
bu00140000 1
 
< 0.1%
bu00140403 1
 
< 0.1%
bu00140105 1
 
< 0.1%
bu00140002 1
 
< 0.1%
bu00140003 1
 
< 0.1%
bu00140005 1
 
< 0.1%
bu00140008 1
 
< 0.1%
bu00140100 1
 
< 0.1%
bu00140101 1
 
< 0.1%
bu00140102 1
 
< 0.1%
Other values (12565) 12565
99.9%
2024-07-05T11:51:10.470137image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 35557
28.3%
1 14799
11.8%
B 12575
 
10.0%
U 12575
 
10.0%
2 7792
 
6.2%
3 7641
 
6.1%
9 6754
 
5.4%
4 6114
 
4.9%
5 5990
 
4.8%
6 5425
 
4.3%
Other values (2) 10528
 
8.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 100600
80.0%
Uppercase Letter 25150
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 35557
35.3%
1 14799
14.7%
2 7792
 
7.7%
3 7641
 
7.6%
9 6754
 
6.7%
4 6114
 
6.1%
5 5990
 
6.0%
6 5425
 
5.4%
7 5372
 
5.3%
8 5156
 
5.1%
Uppercase Letter
ValueCountFrequency (%)
B 12575
50.0%
U 12575
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 100600
80.0%
Latin 25150
 
20.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 35557
35.3%
1 14799
14.7%
2 7792
 
7.7%
3 7641
 
7.6%
9 6754
 
6.7%
4 6114
 
6.1%
5 5990
 
6.0%
6 5425
 
5.4%
7 5372
 
5.3%
8 5156
 
5.1%
Latin
ValueCountFrequency (%)
B 12575
50.0%
U 12575
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 125750
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 35557
28.3%
1 14799
11.8%
B 12575
 
10.0%
U 12575
 
10.0%
2 7792
 
6.2%
3 7641
 
6.1%
9 6754
 
5.4%
4 6114
 
4.9%
5 5990
 
4.8%
6 5425
 
4.3%
Other values (2) 10528
 
8.4%
Distinct11674
Distinct (%)92.8%
Missing0
Missing (%)0.0%
Memory size196.5 KiB
2024-07-05T11:51:10.803560image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length60
Median length49
Mean length15.664254
Min length2

Characters and Unicode

Total characters196978
Distinct characters81
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11232 ?
Unique (%)89.3%

Sample

1st rowBinnenstad-Noord
2nd rowBinnenstad-Zuid
3rd rowBinnenstad-Oost
4th rowBinnenstad-West
5th rowHortusbuurt-Ebbingekwartier
ValueCountFrequency (%)
verspreide 1212
 
5.3%
huizen 1210
 
5.3%
de 799
 
3.5%
en 602
 
2.7%
buitengebied 563
 
2.5%
noord 255
 
1.1%
kern 247
 
1.1%
omgeving 240
 
1.1%
zuid 231
 
1.0%
west 199
 
0.9%
Other values (9709) 17107
75.5%
2024-07-05T11:51:11.408280image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 31319
15.9%
r 15244
 
7.7%
n 13483
 
6.8%
i 11841
 
6.0%
o 10198
 
5.2%
10090
 
5.1%
t 9236
 
4.7%
d 9102
 
4.6%
u 8752
 
4.4%
a 7869
 
4.0%
Other values (71) 69844
35.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 162137
82.3%
Uppercase Letter 21015
 
10.7%
Space Separator 10090
 
5.1%
Dash Punctuation 2429
 
1.2%
Other Punctuation 642
 
0.3%
Decimal Number 492
 
0.2%
Close Punctuation 85
 
< 0.1%
Open Punctuation 85
 
< 0.1%
Math Symbol 3
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 31319
19.3%
r 15244
9.4%
n 13483
 
8.3%
i 11841
 
7.3%
o 10198
 
6.3%
t 9236
 
5.7%
d 9102
 
5.6%
u 8752
 
5.4%
a 7869
 
4.9%
s 7819
 
4.8%
Other values (25) 37274
23.0%
Uppercase Letter
ValueCountFrequency (%)
B 2434
 
11.6%
V 2064
 
9.8%
D 1488
 
7.1%
H 1466
 
7.0%
W 1393
 
6.6%
O 1293
 
6.2%
S 1242
 
5.9%
N 1225
 
5.8%
Z 1133
 
5.4%
K 1054
 
5.0%
Other values (15) 6223
29.6%
Decimal Number
ValueCountFrequency (%)
1 118
24.0%
2 101
20.5%
0 97
19.7%
3 88
17.9%
4 35
 
7.1%
5 21
 
4.3%
6 9
 
1.8%
8 8
 
1.6%
7 8
 
1.6%
9 7
 
1.4%
Other Punctuation
ValueCountFrequency (%)
. 305
47.5%
' 140
21.8%
, 103
 
16.0%
/ 86
 
13.4%
& 4
 
0.6%
" 4
 
0.6%
Space Separator
ValueCountFrequency (%)
10090
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2429
100.0%
Close Punctuation
ValueCountFrequency (%)
) 85
100.0%
Open Punctuation
ValueCountFrequency (%)
( 85
100.0%
Math Symbol
ValueCountFrequency (%)
+ 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 183152
93.0%
Common 13826
 
7.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 31319
17.1%
r 15244
 
8.3%
n 13483
 
7.4%
i 11841
 
6.5%
o 10198
 
5.6%
t 9236
 
5.0%
d 9102
 
5.0%
u 8752
 
4.8%
a 7869
 
4.3%
s 7819
 
4.3%
Other values (50) 58289
31.8%
Common
ValueCountFrequency (%)
10090
73.0%
- 2429
 
17.6%
. 305
 
2.2%
' 140
 
1.0%
1 118
 
0.9%
, 103
 
0.7%
2 101
 
0.7%
0 97
 
0.7%
3 88
 
0.6%
/ 86
 
0.6%
Other values (11) 269
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 196905
> 99.9%
None 73
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 31319
15.9%
r 15244
 
7.7%
n 13483
 
6.8%
i 11841
 
6.0%
o 10198
 
5.2%
10090
 
5.1%
t 9236
 
4.7%
d 9102
 
4.6%
u 8752
 
4.4%
a 7869
 
4.0%
Other values (62) 69771
35.4%
None
ValueCountFrequency (%)
ë 30
41.1%
â 20
27.4%
é 7
 
9.6%
û 6
 
8.2%
ö 4
 
5.5%
ï 3
 
4.1%
ú 1
 
1.4%
ô 1
 
1.4%
á 1
 
1.4%

populationdensityperkm2
Real number (ℝ)

HIGH CORRELATION 

Distinct6027
Distinct (%)47.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3347.323
Minimum2
Maximum48300
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size196.5 KiB
2024-07-05T11:51:11.609168image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile19
Q1186
median2214
Q35180
95-th percentile10136.7
Maximum48300
Range48298
Interquartile range (IQR)4994

Descriptive statistics

Standard deviation3977.5426
Coefficient of variation (CV)1.1882757
Kurtosis9.4642208
Mean3347.323
Median Absolute Deviation (MAD)2140
Skewness2.3372727
Sum42092587
Variance15820845
MonotonicityNot monotonic
2024-07-05T11:51:11.852757image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13 66
 
0.5%
15 54
 
0.4%
14 53
 
0.4%
35 53
 
0.4%
44 50
 
0.4%
22 49
 
0.4%
25 49
 
0.4%
32 48
 
0.4%
18 48
 
0.4%
20 47
 
0.4%
Other values (6017) 12058
95.9%
ValueCountFrequency (%)
2 4
 
< 0.1%
3 6
 
< 0.1%
4 7
 
0.1%
5 19
0.2%
6 16
 
0.1%
7 28
0.2%
8 31
0.2%
9 41
0.3%
10 45
0.4%
11 42
0.3%
ValueCountFrequency (%)
48300 1
< 0.1%
39100 1
< 0.1%
36770 1
< 0.1%
33975 1
< 0.1%
32957 1
< 0.1%
32650 1
< 0.1%
32217 1
< 0.1%
32138 1
< 0.1%
32014 1
< 0.1%
30933 1
< 0.1%

percentage0to15years
Real number (ℝ)

HIGH CORRELATION 

Distinct43
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.744493
Minimum0
Maximum48
Zeros55
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size196.5 KiB
2024-07-05T11:51:12.037117image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q112
median15
Q317
95-th percentile23
Maximum48
Range48
Interquartile range (IQR)5

Descriptive statistics

Standard deviation5.1315915
Coefficient of variation (CV)0.34803445
Kurtosis1.8094438
Mean14.744493
Median Absolute Deviation (MAD)3
Skewness0.37555428
Sum185412
Variance26.333232
MonotonicityNot monotonic
2024-07-05T11:51:12.235456image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
14 1265
 
10.1%
15 1254
 
10.0%
16 1224
 
9.7%
13 1055
 
8.4%
17 991
 
7.9%
12 895
 
7.1%
18 795
 
6.3%
11 722
 
5.7%
19 595
 
4.7%
10 530
 
4.2%
Other values (33) 3249
25.8%
ValueCountFrequency (%)
0 55
 
0.4%
1 48
 
0.4%
2 69
 
0.5%
3 76
 
0.6%
4 104
 
0.8%
5 141
 
1.1%
6 184
1.5%
7 230
1.8%
8 270
2.1%
9 403
3.2%
ValueCountFrequency (%)
48 1
 
< 0.1%
43 1
 
< 0.1%
42 1
 
< 0.1%
40 2
 
< 0.1%
39 2
 
< 0.1%
37 3
 
< 0.1%
36 6
 
< 0.1%
35 14
0.1%
34 17
0.1%
33 12
0.1%

percentage15to25years
Real number (ℝ)

Distinct76
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.337734
Minimum0
Maximum97
Zeros14
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size196.5 KiB
2024-07-05T11:51:12.486024image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7
Q110
median12
Q314
95-th percentile19
Maximum97
Range97
Interquartile range (IQR)4

Descriptive statistics

Standard deviation5.1929422
Coefficient of variation (CV)0.4208992
Kurtosis48.331945
Mean12.337734
Median Absolute Deviation (MAD)2
Skewness4.9429622
Sum155147
Variance26.966649
MonotonicityNot monotonic
2024-07-05T11:51:12.685629image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11 1802
14.3%
12 1689
13.4%
10 1618
12.9%
13 1302
10.4%
9 1108
8.8%
14 994
7.9%
15 765
6.1%
8 625
 
5.0%
16 504
 
4.0%
7 365
 
2.9%
Other values (66) 1803
14.3%
ValueCountFrequency (%)
0 14
 
0.1%
1 17
 
0.1%
2 20
 
0.2%
3 53
 
0.4%
4 64
 
0.5%
5 130
 
1.0%
6 221
 
1.8%
7 365
 
2.9%
8 625
5.0%
9 1108
8.8%
ValueCountFrequency (%)
97 1
< 0.1%
93 1
< 0.1%
80 2
< 0.1%
79 1
< 0.1%
77 1
< 0.1%
76 2
< 0.1%
73 1
< 0.1%
72 2
< 0.1%
71 1
< 0.1%
70 2
< 0.1%

percentage25to45years
Real number (ℝ)

HIGH CORRELATION 

Distinct76
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.329861
Minimum0
Maximum86
Zeros9
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size196.5 KiB
2024-07-05T11:51:12.868854image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile12
Q117
median21
Q325
95-th percentile38
Maximum86
Range86
Interquartile range (IQR)8

Descriptive statistics

Standard deviation8.1404862
Coefficient of variation (CV)0.36455606
Kurtosis4.4761679
Mean22.329861
Median Absolute Deviation (MAD)4
Skewness1.4734382
Sum280798
Variance66.267515
MonotonicityNot monotonic
2024-07-05T11:51:13.153595image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20 927
 
7.4%
21 925
 
7.4%
19 895
 
7.1%
22 834
 
6.6%
23 788
 
6.3%
18 760
 
6.0%
17 723
 
5.7%
24 648
 
5.2%
16 593
 
4.7%
25 513
 
4.1%
Other values (66) 4969
39.5%
ValueCountFrequency (%)
0 9
 
0.1%
1 3
 
< 0.1%
2 8
 
0.1%
3 8
 
0.1%
4 14
 
0.1%
5 20
 
0.2%
6 32
 
0.3%
7 42
0.3%
8 43
0.3%
9 80
0.6%
ValueCountFrequency (%)
86 1
 
< 0.1%
78 1
 
< 0.1%
77 1
 
< 0.1%
76 1
 
< 0.1%
75 1
 
< 0.1%
74 1
 
< 0.1%
72 1
 
< 0.1%
71 4
< 0.1%
68 3
< 0.1%
67 4
< 0.1%

percentage45to65years
Real number (ℝ)

HIGH CORRELATION 

Distinct57
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.717535
Minimum0
Maximum59
Zeros9
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size196.5 KiB
2024-07-05T11:51:13.334965image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile19
Q126
median30
Q334
95-th percentile40
Maximum59
Range59
Interquartile range (IQR)8

Descriptive statistics

Standard deviation6.504117
Coefficient of variation (CV)0.21886462
Kurtosis1.59393
Mean29.717535
Median Absolute Deviation (MAD)4
Skewness-0.31200495
Sum373698
Variance42.303538
MonotonicityNot monotonic
2024-07-05T11:51:13.518525image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29 866
 
6.9%
28 864
 
6.9%
31 834
 
6.6%
30 825
 
6.6%
27 823
 
6.5%
32 753
 
6.0%
26 743
 
5.9%
33 671
 
5.3%
34 632
 
5.0%
25 606
 
4.8%
Other values (47) 4958
39.4%
ValueCountFrequency (%)
0 9
0.1%
1 10
0.1%
2 12
0.1%
3 7
0.1%
4 12
0.1%
5 4
 
< 0.1%
6 3
 
< 0.1%
7 10
0.1%
8 7
0.1%
9 13
0.1%
ValueCountFrequency (%)
59 2
 
< 0.1%
55 1
 
< 0.1%
54 2
 
< 0.1%
53 5
 
< 0.1%
52 6
 
< 0.1%
51 5
 
< 0.1%
50 7
 
0.1%
49 17
0.1%
48 25
0.2%
47 24
0.2%

percentage65yearsorolder
Real number (ℝ)

HIGH CORRELATION 

Distinct88
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.880159
Minimum0
Maximum100
Zeros38
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size196.5 KiB
2024-07-05T11:51:13.751735image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8
Q115
median20
Q325
95-th percentile37
Maximum100
Range100
Interquartile range (IQR)10

Descriptive statistics

Standard deviation9.3775178
Coefficient of variation (CV)0.44911142
Kurtosis6.2944163
Mean20.880159
Median Absolute Deviation (MAD)5
Skewness1.4384875
Sum262568
Variance87.93784
MonotonicityNot monotonic
2024-07-05T11:51:14.010504image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20 750
 
6.0%
19 712
 
5.7%
21 691
 
5.5%
18 678
 
5.4%
22 638
 
5.1%
17 635
 
5.0%
23 629
 
5.0%
16 577
 
4.6%
24 529
 
4.2%
25 515
 
4.1%
Other values (78) 6221
49.5%
ValueCountFrequency (%)
0 38
 
0.3%
1 39
 
0.3%
2 63
 
0.5%
3 67
 
0.5%
4 61
 
0.5%
5 92
0.7%
6 116
0.9%
7 142
1.1%
8 196
1.6%
9 191
1.5%
ValueCountFrequency (%)
100 1
 
< 0.1%
98 2
< 0.1%
96 1
 
< 0.1%
95 1
 
< 0.1%
89 3
< 0.1%
88 2
< 0.1%
87 1
 
< 0.1%
84 1
 
< 0.1%
83 1
 
< 0.1%
82 2
< 0.1%

percentageonepersonhouseholds
Real number (ℝ)

HIGH CORRELATION 

Distinct125
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.333105
Minimum0
Maximum100
Zeros7
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size196.5 KiB
2024-07-05T11:51:14.204604image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile14
Q122
median29
Q340
95-th percentile60
Maximum100
Range100
Interquartile range (IQR)18

Descriptive statistics

Standard deviation14.507888
Coefficient of variation (CV)0.44870074
Kurtosis1.4700209
Mean32.333105
Median Absolute Deviation (MAD)8
Skewness1.05833
Sum406588.8
Variance210.47883
MonotonicityNot monotonic
2024-07-05T11:51:14.385341image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25 457
 
3.6%
28 441
 
3.5%
24 438
 
3.5%
29 437
 
3.5%
27 436
 
3.5%
30 436
 
3.5%
26 434
 
3.5%
23 421
 
3.3%
22 414
 
3.3%
31 392
 
3.1%
Other values (115) 8269
65.8%
ValueCountFrequency (%)
0 7
 
0.1%
1 1
 
< 0.1%
2 2
 
< 0.1%
3 7
 
0.1%
4 7
 
0.1%
5 19
 
0.2%
6 22
 
0.2%
7 33
0.3%
8 31
0.2%
9 56
0.4%
ValueCountFrequency (%)
100 2
 
< 0.1%
99 2
 
< 0.1%
98 2
 
< 0.1%
97 4
< 0.1%
96 3
 
< 0.1%
95 2
 
< 0.1%
94 8
0.1%
93 3
 
< 0.1%
92 5
< 0.1%
91 8
0.1%

percentagehouseholdswithoutchildren
Real number (ℝ)

HIGH CORRELATION 

Distinct96
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.358775
Minimum0
Maximum75
Zeros5
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size196.5 KiB
2024-07-05T11:51:14.608145image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile19
Q127
median32
Q337
95-th percentile46
Maximum75
Range75
Interquartile range (IQR)10

Descriptive statistics

Standard deviation8.2532069
Coefficient of variation (CV)0.25505313
Kurtosis0.83939241
Mean32.358775
Median Absolute Deviation (MAD)5
Skewness0.10362577
Sum406911.6
Variance68.115425
MonotonicityNot monotonic
2024-07-05T11:51:14.902049image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33 700
 
5.6%
35 698
 
5.6%
34 665
 
5.3%
32 648
 
5.2%
30 608
 
4.8%
36 600
 
4.8%
29 584
 
4.6%
31 562
 
4.5%
37 561
 
4.5%
28 519
 
4.1%
Other values (86) 6430
51.1%
ValueCountFrequency (%)
0 5
 
< 0.1%
1 2
 
< 0.1%
2 7
0.1%
3 5
 
< 0.1%
4 7
0.1%
5 5
 
< 0.1%
6 13
0.1%
7 9
0.1%
8 6
< 0.1%
8.6 1
 
< 0.1%
ValueCountFrequency (%)
75 1
 
< 0.1%
73 1
 
< 0.1%
69 1
 
< 0.1%
67 1
 
< 0.1%
66 1
 
< 0.1%
65 1
 
< 0.1%
64 6
< 0.1%
63 4
< 0.1%
62 3
< 0.1%
61 5
< 0.1%

percentagehouseholdswithchildren
Real number (ℝ)

HIGH CORRELATION 

Distinct107
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.326266
Minimum0
Maximum84
Zeros24
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size196.5 KiB
2024-07-05T11:51:15.133561image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile14
Q129
median36
Q342
95-th percentile55
Maximum84
Range84
Interquartile range (IQR)13

Descriptive statistics

Standard deviation11.862697
Coefficient of variation (CV)0.33580387
Kurtosis0.67380605
Mean35.326266
Median Absolute Deviation (MAD)7
Skewness-0.092399529
Sum444227.8
Variance140.72358
MonotonicityNot monotonic
2024-07-05T11:51:15.334261image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36 551
 
4.4%
39 519
 
4.1%
37 514
 
4.1%
38 512
 
4.1%
35 503
 
4.0%
32 494
 
3.9%
34 489
 
3.9%
40 457
 
3.6%
33 450
 
3.6%
41 424
 
3.4%
Other values (97) 7662
60.9%
ValueCountFrequency (%)
0 24
0.2%
1 23
0.2%
2 25
0.2%
3 22
 
0.2%
4 24
0.2%
5 30
0.2%
6 56
0.4%
6.6 1
 
< 0.1%
7 38
0.3%
8 41
0.3%
ValueCountFrequency (%)
84 1
 
< 0.1%
82 1
 
< 0.1%
81 1
 
< 0.1%
80 3
< 0.1%
79 3
< 0.1%
78 3
< 0.1%
77 1
 
< 0.1%
76 1
 
< 0.1%
75 7
0.1%
74 4
< 0.1%

percentagewesternmigrationbackground
Real number (ℝ)

HIGH CORRELATION 

Distinct66
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.1798807
Minimum0
Maximum91
Zeros101
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size196.5 KiB
2024-07-05T11:51:15.517957image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q15
median8
Q311
95-th percentile21
Maximum91
Range91
Interquartile range (IQR)6

Descriptive statistics

Standard deviation6.5692716
Coefficient of variation (CV)0.71561623
Kurtosis14.203219
Mean9.1798807
Median Absolute Deviation (MAD)3
Skewness2.6252983
Sum115437
Variance43.155329
MonotonicityNot monotonic
2024-07-05T11:51:15.718968image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 1106
 
8.8%
7 1069
 
8.5%
4 1055
 
8.4%
8 1054
 
8.4%
5 1007
 
8.0%
9 961
 
7.6%
10 869
 
6.9%
3 771
 
6.1%
11 718
 
5.7%
12 554
 
4.4%
Other values (56) 3411
27.1%
ValueCountFrequency (%)
0 101
 
0.8%
1 250
 
2.0%
2 511
4.1%
3 771
6.1%
4 1055
8.4%
5 1007
8.0%
6 1106
8.8%
7 1069
8.5%
8 1054
8.4%
9 961
7.6%
ValueCountFrequency (%)
91 1
< 0.1%
83 1
< 0.1%
80 1
< 0.1%
78 1
< 0.1%
72 1
< 0.1%
70 1
< 0.1%
69 1
< 0.1%
64 1
< 0.1%
61 2
< 0.1%
60 1
< 0.1%

percentagenonwesternmigrationbackground
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct90
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.6322068
Minimum0
Maximum95
Zeros1254
Zeros (%)10.0%
Negative0
Negative (%)0.0%
Memory size196.5 KiB
2024-07-05T11:51:15.907827image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median4
Q311
95-th percentile33
Maximum95
Range95
Interquartile range (IQR)9

Descriptive statistics

Standard deviation11.720358
Coefficient of variation (CV)1.3577476
Kurtosis9.2867915
Mean8.6322068
Median Absolute Deviation (MAD)3
Skewness2.7228449
Sum108550
Variance137.36679
MonotonicityNot monotonic
2024-07-05T11:51:16.090263image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1584
12.6%
2 1472
11.7%
3 1266
 
10.1%
0 1254
 
10.0%
4 994
 
7.9%
5 730
 
5.8%
6 557
 
4.4%
7 468
 
3.7%
8 408
 
3.2%
9 351
 
2.8%
Other values (80) 3491
27.8%
ValueCountFrequency (%)
0 1254
10.0%
1 1584
12.6%
2 1472
11.7%
3 1266
10.1%
4 994
7.9%
5 730
5.8%
6 557
 
4.4%
7 468
 
3.7%
8 408
 
3.2%
9 351
 
2.8%
ValueCountFrequency (%)
95 1
 
< 0.1%
93 2
< 0.1%
91 3
< 0.1%
90 1
 
< 0.1%
87 1
 
< 0.1%
86 2
< 0.1%
84 1
 
< 0.1%
83 1
 
< 0.1%
82 2
< 0.1%
80 3
< 0.1%

percentagemen
Real number (ℝ)

Distinct314
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.641416
Minimum28
Maximum104
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size196.5 KiB
2024-07-05T11:51:16.284455image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum28
5-th percentile46.1
Q148.8
median50.2
Q352
95-th percentile56.2
Maximum104
Range76
Interquartile range (IQR)3.2

Descriptive statistics

Standard deviation3.7801781
Coefficient of variation (CV)0.07464598
Kurtosis26.607051
Mean50.641416
Median Absolute Deviation (MAD)1.6
Skewness2.7389833
Sum636815.8
Variance14.289746
MonotonicityNot monotonic
2024-07-05T11:51:16.503742image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50 1004
 
8.0%
50.3 225
 
1.8%
49.7 207
 
1.6%
49.3 206
 
1.6%
50.5 200
 
1.6%
50.8 199
 
1.6%
50.6 196
 
1.6%
50.7 194
 
1.5%
51.2 192
 
1.5%
49.4 188
 
1.5%
Other values (304) 9764
77.6%
ValueCountFrequency (%)
28 1
< 0.1%
29.5 1
< 0.1%
30 2
< 0.1%
30.8 1
< 0.1%
32.5 1
< 0.1%
32.9 1
< 0.1%
33.3 1
< 0.1%
33.9 1
< 0.1%
34 1
< 0.1%
35.4 1
< 0.1%
ValueCountFrequency (%)
104 1
 
< 0.1%
100 4
< 0.1%
96.2 1
 
< 0.1%
93.3 1
 
< 0.1%
92.9 1
 
< 0.1%
89.7 1
 
< 0.1%
87.7 1
 
< 0.1%
87.1 1
 
< 0.1%
86.4 1
 
< 0.1%
85.7 1
 
< 0.1%

Interactions

2024-07-05T11:51:07.087630image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:46.845214image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:49.156652image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:51.756923image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:53.475756image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:55.279153image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:56.907583image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:58.556927image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:00.523311image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:02.206523image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:03.772468image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:05.505297image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:07.221004image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:47.050348image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:49.310279image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:51.902451image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:53.660604image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:55.417112image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:57.050704image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:58.690422image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:00.676786image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:02.339085image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:03.956072image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:05.622349image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:07.354900image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:47.190789image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:49.441799image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:52.036211image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:53.827899image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:55.548657image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:57.174294image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:59.173335image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:00.806379image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:02.472811image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:04.190648image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:05.755178image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:07.487815image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:47.353559image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:49.611125image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:52.172861image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:53.970599image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:55.691379image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:57.307420image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:59.290136image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:00.952630image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:02.605964image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:04.321857image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:05.888468image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:07.621110image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:47.553871image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:49.748912image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:52.288771image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:54.098664image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:55.824584image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:57.424780image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:59.423391image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:01.072497image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:02.724485image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:04.441248image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:06.021427image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:07.754968image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:47.770485image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:49.903887image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:52.442448image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:54.276915image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:55.970235image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:57.573923image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:59.556854image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:01.223400image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:02.855665image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:04.571667image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:06.154901image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:08.304157image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:47.953520image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:50.058222image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:52.642957image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:54.494204image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:56.106819image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:57.707599image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:59.690153image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:01.355993image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:02.988676image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:04.705586image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:06.303277image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:08.453666image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:48.123938image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:50.196261image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:52.786958image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:54.630736image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:56.242025image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:57.891078image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:59.856694image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:01.489694image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:03.124230image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:04.840606image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:06.421791image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:08.603676image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:48.348530image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:50.359043image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:52.920579image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:54.762257image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:56.374069image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:58.036996image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:59.989777image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:01.639470image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:03.272370image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:04.972275image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:06.571198image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:08.737516image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:48.587261image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:50.497141image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:53.051413image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:54.903672image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:56.508134image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:58.157195image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:00.123353image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:01.772649image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:03.389108image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:05.104796image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:06.687747image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:08.870445image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:48.794066image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:51.479313image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:53.191991image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:55.020607image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:56.641807image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:58.290982image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:00.263569image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:01.922577image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:03.522281image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:05.240729image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:06.821166image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:09.003547image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:48.953465image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:51.609748image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:53.328607image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:55.147653image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:56.757840image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:50:58.423898image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:00.390348image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:02.056602image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:03.638561image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:05.354773image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T11:51:06.954261image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Correlations

2024-07-05T11:51:16.634246image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
percentage0to15yearspercentage15to25yearspercentage25to45yearspercentage45to65yearspercentage65yearsorolderpercentagehouseholdswithchildrenpercentagehouseholdswithoutchildrenpercentagemenpercentagenonwesternmigrationbackgroundpercentageonepersonhouseholdspercentagewesternmigrationbackgroundpopulationdensityperkm2
percentage0to15years1.0000.0510.263-0.220-0.4510.662-0.242-0.0240.082-0.367-0.1940.112
percentage15to25years0.0511.000-0.0470.157-0.4840.361-0.2100.257-0.064-0.198-0.161-0.112
percentage25to45years0.263-0.0471.000-0.584-0.538-0.103-0.587-0.0000.5770.3970.3740.523
percentage45to65years-0.2200.157-0.5841.0000.0260.2610.4680.322-0.498-0.457-0.272-0.510
percentage65yearsorolder-0.451-0.484-0.5380.0261.000-0.4570.483-0.295-0.2080.148-0.039-0.117
percentagehouseholdswithchildren0.6620.361-0.1030.261-0.4571.0000.0110.193-0.263-0.794-0.426-0.245
percentagehouseholdswithoutchildren-0.242-0.210-0.5870.4680.4830.0111.0000.119-0.555-0.525-0.360-0.468
percentagemen-0.0240.257-0.0000.322-0.2950.1930.1191.000-0.286-0.249-0.160-0.414
percentagenonwesternmigrationbackground0.082-0.0640.577-0.498-0.208-0.263-0.555-0.2861.0000.5220.5820.724
percentageonepersonhouseholds-0.367-0.1980.397-0.4570.148-0.794-0.525-0.2490.5221.0000.5410.468
percentagewesternmigrationbackground-0.194-0.1610.374-0.272-0.039-0.426-0.360-0.1600.5820.5411.0000.468
populationdensityperkm20.112-0.1120.523-0.510-0.117-0.245-0.468-0.4140.7240.4680.4681.000

Missing values

2024-07-05T11:51:09.220131image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
A simple visualization of nullity by column.
2024-07-05T11:51:09.570379image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

neighborhoodcodeneighborhoodnamepopulationdensityperkm2percentage0to15yearspercentage15to25yearspercentage25to45yearspercentage45to65yearspercentage65yearsorolderpercentageonepersonhouseholdspercentagehouseholdswithoutchildrenpercentagehouseholdswithchildrenpercentagewesternmigrationbackgroundpercentagenonwesternmigrationbackgroundpercentagemen
0BU00140000Binnenstad-Noord12192.02.047.035.011.06.082.016.03.019.010.052.8
1BU00140001Binnenstad-Zuid11651.02.046.034.011.07.081.016.03.018.012.053.1
2BU00140002Binnenstad-Oost15441.03.042.035.011.09.079.017.04.018.014.052.5
3BU00140003Binnenstad-West17200.02.038.041.010.010.080.017.03.020.010.054.7
5BU00140005Hortusbuurt-Ebbingekwartier13019.05.037.033.015.010.078.016.06.019.011.050.1
7BU00140008Stationsgebied2438.00.061.010.010.018.087.012.02.044.013.043.8
8BU00140100De Meeuwen6445.09.028.023.021.018.067.020.013.028.09.049.6
9BU00140101Oosterpoort12473.05.030.036.018.011.070.022.08.016.08.049.9
10BU00140102Herewegbuurt9819.07.032.038.016.07.069.022.09.016.09.047.5
11BU00140103Rivierenbuurt8291.04.030.038.014.013.071.022.06.015.09.049.1
neighborhoodcodeneighborhoodnamepopulationdensityperkm2percentage0to15yearspercentage15to25yearspercentage25to45yearspercentage45to65yearspercentage65yearsorolderpercentageonepersonhouseholdspercentagehouseholdswithoutchildrenpercentagehouseholdswithchildrenpercentagewesternmigrationbackgroundpercentagenonwesternmigrationbackgroundpercentagemen
14070BU19791883Huizinge693.013.015.011.033.028.046.022.032.05.00.052.4
14071BU19791884Toornwerd369.012.010.014.039.025.017.050.033.08.02.050.0
14072BU19791885Buitengebied Noord-Middelstum13.022.09.022.028.019.018.040.042.03.01.054.1
14073BU19791986't Zandt1408.017.09.023.032.018.038.030.032.05.01.053.1
14074BU19791987Zeerijp875.017.015.021.035.013.028.034.038.05.01.053.6
14075BU19791988Zijldijk1164.013.010.020.037.020.040.031.029.02.01.050.0
14076BU19791989Leermens700.011.010.018.038.024.027.047.027.03.01.051.4
14077BU19791990Oosterwijtwerd1470.07.010.015.038.029.030.046.024.06.01.050.0
14078BU19791991Eenum423.06.08.012.039.035.023.052.025.05.00.055.6
14079BU19791992Buitengebied Noord-'t Zandt12.013.011.018.038.021.023.045.032.02.01.056.8